Toward Resilience in Mixed Critical Industrial Control Systems: A Multi-Disciplinary View

Abstract

Future industrial control systems face the need for being highly adaptive, productive, and efficient, yet providing a high level of safety towards operating staff, environment, and machinery. These demands call for the joint consideration of resilience and mixed criticality to exploit previously untapped redundancy potentials. Hereby, resilience combines detection, decision-making, adaption to, and recovery from unforeseeable or malicious events in an autonomous manner. Enabling the consideration of functionalities with different criticalities, mixed criticality allows prioritizing safety-relevant over uncritical functions. While both concepts on their own feature a huge research branch throughout various disciplines of engineering-related fields, the synergies of both paradigms in a multi-disciplinary context are commonly overlooked. In industrial control, consolidating these mechanisms while preserving functional safety requirements under limited resources is a significant challenge. In this contribution, we provide a multidisciplinary perspective of the concepts and mechanisms that enable criticality-aware resilience, in particular with respect to system design, communication, control, and security. Thereby, we envision a highly flexible, autonomous, and scalable paradigm for industrial control systems, identify potentials along the different domains, and identify future research directions. Our results indicate that jointly employing mixed criticality and resilience has the potential to increase the overall systems efficiency, reliability, and flexibility, even against unanticipated or malicious events. Thus, for future industrial systems, mixed criticality-aware resilience is a crucial factor towards autonomy and increasing the overall system performance.

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

“…Furthermore, when a behavior is not anticipated our handover scheme instantaneously gets triggered when the QoPE of the user drops. This enables a resilient [30] decision making mechanism that can flexibly interact with the environment regardless of its dynamics and the accuracy of our estimated and predicted sensing parameters.…”